Computerized Property Prediction and Process Planning in Heat Treatment of Steels

Gergely, M.; Somogyi, Sz.; Kohlheb, R.

doi:10.4028/www.scientific.net/msf.163-165.657

Cited by 8 publications

(10 citation statements)

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“…On the contrary, present thermo-metallurgical, non-linear, transient, finite-elements models allow to perform coupled simulations of the heat conduction and of the metallurgical phase transformations [4,5]; therefore precise temperature-and phase-dependent thermophysical properties can be employed in order to seek more accurate results. 2 Because these data are seldom available for a specific steel grade, it is useful to evaluate which property is more influent upon the quench process, and therefore is worth to be more accurately assessed.…”

Section: Introductionmentioning

confidence: 99%

Thermal diffusivity measurements of metastable austenite during continuous cooling

Matteis¹,

Campagnoli

Firrao

et al. 2008

International Journal of Thermal Sciences

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The thermal diffusivity of the metastable undercooled austenite is relevant for the quantitative analysis of the carbon and low-alloy steel quench. The standard laser-flash method requires prior thermal equilibrium between the sample and the furnace, which may not be possible to achieve without allowing the metastable phase to transform. Nevertheless, depending upon the steel's hardenability, the thermal transient due to a laser pulse may be much shorter than a cooling transient sufficiently steep to prevent the transformation of the austenite. In one such case, flash measurements were performed during continuous sample cooling and the thermal diffusivity of the metastable austenite was determined by using an extension of the standard analytical model. The adopted analytical model and data reduction procedure are described and the limitations and uncertainties of this method are discussed, also with the aid of a non-linear numerical simulation. The measured thermal diffusivity of the undercooled low-alloy austenite decreases linearly from 5.4 · 10 −6 m 2 s −1 at 1133 K to 4.3 · 10 −6 m 2 s −1 at 755 K; this trend is in broad agreement with one previous set of measurements upon a low-alloy undercooled austenite and with a large number of previous standard measurements upon stable (high-alloy) austenitic stainless steels.

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Section: Introductionmentioning

confidence: 99%

Thermal diffusivity measurements of metastable austenite during continuous cooling

Matteis¹,

Campagnoli

Firrao

et al. 2008

International Journal of Thermal Sciences

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“…In this study is assumed constant emissivity (0.45) during welding simulations 3,[9][10][11][12][13][14][15][16][17][18][19][20][21][22][23] . The specific mass appeared in Equation 1 is assumed constant as 7700 kg.m -3 .…”

Section: Numerical Proceduresmentioning

confidence: 99%

“…Models capable of predicting the local conditions and possibility of deleterious phases being formed are welcome and can drive good practice for avoiding the non-uniformity of properties [12][13][14][15][16][17][18][19][20][21][22] . This paper is focused on this challenge.…”

Section: Introductionmentioning

confidence: 99%

Effects of Local Heat Input Conditions on the Thermophysical Properties of Super Duplex Stainless Steels (SDSS)

et al. 2018

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The properties of the super duplex stainless steels (SDSS) are strongly affected by the thermal history imposed by welding procedures. The controlled dual phase microstructure (ferrite and austenite) guarantee excellent mechanical properties such as mechanical strength and corrosion resistance, in addition to small thermal expansion coefficient and high thermal conductivity. In this paper, we newly proposed a model able to predict the thermal history of the welding pieces coupled with local mechanical properties developed during welding procedure that combine the effects of temperature and phase changes during welding. We applied inverse method to fit the thermophysical parameters based on measured data. The model was verified by comparing measured and predicted temperature profiles using thermocouples located within the heat affected zone. Thus, an inverse method was implemented to obtain the parameters fitting for the grain growth evolution compatible with the final microstructure and grain size measured using SEM images and stereological techniques. We demonstrated that very small amount of non-equilibrium deleterious phases and nanosized precipitated are expected during the welding procedures depending upon the local conditions of temperature, compositions and alloying dilution evolutions.

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“…There are many methods for predicting hardness distribution of quenched steel based on linear mixing rule of microstructure, carbon content, individual isothermal hardness of microstructural elements and cooling time only (Gergely et al, 1991;Fluhrer, 2005). However, one of the most important factors for predicting the hardness distribution of a quenched steel is the input data of a representative cooling characteristic that is relevant for phase transformation from austenite (parent phase) to transformed products (child phase).…”

Section: Hardness Predictionmentioning

confidence: 99%

Numerical simulation and experimental verification of carburizing-quenching process of SCr420H steel helical gear

Sugianto

Narazaki

Kogawara

et al. 2009

Journal of Materials Processing Technology

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Computerized Property Prediction and Process Planning in Heat Treatment of Steels

Cited by 8 publications

References 0 publications

Thermal diffusivity measurements of metastable austenite during continuous cooling

Thermal diffusivity measurements of metastable austenite during continuous cooling

Effects of Local Heat Input Conditions on the Thermophysical Properties of Super Duplex Stainless Steels (SDSS)

Numerical simulation and experimental verification of carburizing-quenching process of SCr420H steel helical gear

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